{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Han\Desktop\document\불평등 연구\주제14_내로남불_교육\social_sciences\data\analysis_1.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}10 Nov 2024, 13:15:18

{com}. meoprobit family_edu class education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    12,877
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        16
{col 63}{txt}avg{col 67}={res}{col 69}     378.7
{col 63}{txt}max{col 67}={res}{col 69}     1,005

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    88.48
{txt}Log likelihood = {res}-17428.301{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  family_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2}  .000863{col 26}{space 2} .0061276{col 37}{space 1}    0.14{col 46}{space 3}0.888{col 54}{space 4}-.0111469{col 67}{space 3} .0128728
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0508527{col 26}{space 2} .0076748{col 37}{space 1}    6.63{col 46}{space 3}0.000{col 54}{space 4} .0358103{col 67}{space 3} .0658951
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0213999{col 26}{space 2} .0063279{col 37}{space 1}    3.38{col 46}{space 3}0.001{col 54}{space 4} .0089975{col 67}{space 3} .0338023
{txt}{space 6}gender {c |}{col 14}{res}{space 2} -.054687{col 26}{space 2} .0188873{col 37}{space 1}   -2.90{col 46}{space 3}0.004{col 54}{space 4}-.0917054{col 67}{space 3}-.0176686
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0039194{col 26}{space 2} .0194362{col 37}{space 1}   -0.20{col 46}{space 3}0.840{col 54}{space 4}-.0420136{col 67}{space 3} .0341748
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .0280801{col 26}{space 2} .0215642{col 37}{space 1}    1.30{col 46}{space 3}0.193{col 54}{space 4}-.0141848{col 67}{space 3} .0703451
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0366371{col 26}{space 2} .0080881{col 37}{space 1}    4.53{col 46}{space 3}0.000{col 54}{space 4} .0207848{col 67}{space 3} .0524894
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.0368865{col 26}{space 2} .0286709{col 37}{space 1}   -1.29{col 46}{space 3}0.198{col 54}{space 4}-.0930805{col 67}{space 3} .0193076
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-1.380882{col 26}{space 2} .0817122{col 37}{space 1}  -16.90{col 46}{space 3}0.000{col 54}{space 4}-1.541035{col 67}{space 3} -1.22073
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-.4337902{col 26}{space 2} .0807942{col 37}{space 1}   -5.37{col 46}{space 3}0.000{col 54}{space 4} -.592144{col 67}{space 3}-.2754364
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}  .654124{col 26}{space 2} .0809361{col 37}{space 1}    8.08{col 46}{space 3}0.000{col 54}{space 4} .4954922{col 67}{space 3} .8127558
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.811935{col 26}{space 2} .0818597{col 37}{space 1}   22.13{col 46}{space 3}0.000{col 54}{space 4} 1.651493{col 67}{space 3} 1.972377
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1161291{col 26}{space 2} .0303304{col 54}{space 4} .0696027{col 67}{space 3} .1937566
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1002.21{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit family_edu class income education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,544
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        12
{col 63}{txt}avg{col 67}={res}{col 69}     339.5
{col 63}{txt}max{col 67}={res}{col 69}       941

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}   100.33
{txt}Log likelihood = {res}-15512.307{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  family_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0006302{col 26}{space 2} .0066202{col 37}{space 1}    0.10{col 46}{space 3}0.924{col 54}{space 4}-.0123451{col 67}{space 3} .0136055
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0001958{col 26}{space 2} .0100736{col 37}{space 1}    0.02{col 46}{space 3}0.984{col 54}{space 4}-.0195481{col 67}{space 3} .0199396
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0579757{col 26}{space 2} .0084232{col 37}{space 1}    6.88{col 46}{space 3}0.000{col 54}{space 4} .0414664{col 67}{space 3} .0744849
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0216655{col 26}{space 2} .0066947{col 37}{space 1}    3.24{col 46}{space 3}0.001{col 54}{space 4} .0085441{col 67}{space 3} .0347869
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.0595315{col 26}{space 2}  .020444{col 37}{space 1}   -2.91{col 46}{space 3}0.004{col 54}{space 4} -.099601{col 67}{space 3}-.0194621
{txt}{space 5}marital {c |}{col 14}{res}{space 2} .0003334{col 26}{space 2} .0206323{col 37}{space 1}    0.02{col 46}{space 3}0.987{col 54}{space 4}-.0401052{col 67}{space 3} .0407721
{txt}{space 4}religion {c |}{col 14}{res}{space 2}  .022035{col 26}{space 2} .0227376{col 37}{space 1}    0.97{col 46}{space 3}0.332{col 54}{space 4}-.0225299{col 67}{space 3} .0665998
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0418936{col 26}{space 2} .0085818{col 37}{space 1}    4.88{col 46}{space 3}0.000{col 54}{space 4} .0250736{col 67}{space 3} .0587136
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.0403285{col 26}{space 2} .0299321{col 37}{space 1}   -1.35{col 46}{space 3}0.178{col 54}{space 4}-.0989944{col 67}{space 3} .0183373
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-1.372349{col 26}{space 2} .0864985{col 37}{space 1}  -15.87{col 46}{space 3}0.000{col 54}{space 4}-1.541883{col 67}{space 3}-1.202816
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-.4154773{col 26}{space 2} .0855248{col 37}{space 1}   -4.86{col 46}{space 3}0.000{col 54}{space 4}-.5831027{col 67}{space 3}-.2478518
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} .6912009{col 26}{space 2} .0857037{col 37}{space 1}    8.07{col 46}{space 3}0.000{col 54}{space 4} .5232248{col 67}{space 3}  .859177
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.863347{col 26}{space 2} .0867248{col 37}{space 1}   21.49{col 46}{space 3}0.000{col 54}{space 4}  1.69337{col 67}{space 3} 2.033325
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1235825{col 26}{space 2}  .032272{col 54}{space 4} .0740758{col 67}{space 3} .2061756
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 950.80{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit family_edu class income employer high_skilled education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,544
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        12
{col 63}{txt}avg{col 67}={res}{col 69}     339.5
{col 63}{txt}max{col 67}={res}{col 69}       941

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   111.83
{txt}Log likelihood = {res}-15506.544{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  family_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2}-.0010759{col 26}{space 2} .0066536{col 37}{space 1}   -0.16{col 46}{space 3}0.872{col 54}{space 4}-.0141168{col 67}{space 3} .0119649
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0064241{col 26}{space 2} .0102949{col 37}{space 1}   -0.62{col 46}{space 3}0.533{col 54}{space 4}-.0266016{col 67}{space 3} .0137535
{txt}{space 4}employer {c |}{col 14}{res}{space 2}-.0707316{col 26}{space 2}  .063819{col 37}{space 1}   -1.11{col 46}{space 3}0.268{col 54}{space 4}-.1958145{col 67}{space 3} .0543513
{txt}high_skilled {c |}{col 14}{res}{space 2} .0782516{col 26}{space 2} .0241314{col 37}{space 1}    3.24{col 46}{space 3}0.001{col 54}{space 4} .0309549{col 67}{space 3} .1255482
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0463358{col 26}{space 2} .0091622{col 37}{space 1}    5.06{col 46}{space 3}0.000{col 54}{space 4} .0283782{col 67}{space 3} .0642933
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0195274{col 26}{space 2} .0067331{col 37}{space 1}    2.90{col 46}{space 3}0.004{col 54}{space 4} .0063309{col 67}{space 3}  .032724
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.0655545{col 26}{space 2} .0205235{col 37}{space 1}   -3.19{col 46}{space 3}0.001{col 54}{space 4}-.1057799{col 67}{space 3}-.0253292
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0006628{col 26}{space 2} .0206366{col 37}{space 1}   -0.03{col 46}{space 3}0.974{col 54}{space 4}-.0411097{col 67}{space 3} .0397842
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .0234258{col 26}{space 2} .0227463{col 37}{space 1}    1.03{col 46}{space 3}0.303{col 54}{space 4}-.0211562{col 67}{space 3} .0680077
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0408876{col 26}{space 2} .0085876{col 37}{space 1}    4.76{col 46}{space 3}0.000{col 54}{space 4} .0240562{col 67}{space 3} .0577191
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.0367842{col 26}{space 2} .0299997{col 37}{space 1}   -1.23{col 46}{space 3}0.220{col 54}{space 4}-.0955825{col 67}{space 3} .0220142
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2} -1.42179{col 26}{space 2} .0879632{col 37}{space 1}  -16.16{col 46}{space 3}0.000{col 54}{space 4}-1.594195{col 67}{space 3}-1.249386
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-.4639365{col 26}{space 2} .0869532{col 37}{space 1}   -5.34{col 46}{space 3}0.000{col 54}{space 4}-.6343617{col 67}{space 3}-.2935113
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} .6434579{col 26}{space 2} .0870931{col 37}{space 1}    7.39{col 46}{space 3}0.000{col 54}{space 4} .4727586{col 67}{space 3} .8141572
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.815843{col 26}{space 2} .0880846{col 37}{space 1}   20.61{col 46}{space 3}0.000{col 54}{space 4} 1.643201{col 67}{space 3} 1.988486
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1245247{col 26}{space 2}  .032516{col 54}{space 4}  .074643{col 67}{space 3}  .207741
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 949.38{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. estimates store edu_1

. meoprobit family_edu class education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,514
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     338.6
{col 63}{txt}max{col 67}={res}{col 69}     1,202

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}   137.99
{txt}Log likelihood = {res}-15512.555{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  family_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0180657{col 26}{space 2} .0067887{col 37}{space 1}    2.66{col 46}{space 3}0.008{col 54}{space 4} .0047601{col 67}{space 3} .0313713
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0511843{col 26}{space 2} .0083349{col 37}{space 1}    6.14{col 46}{space 3}0.000{col 54}{space 4} .0348482{col 67}{space 3} .0675204
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0463604{col 26}{space 2}  .006731{col 37}{space 1}    6.89{col 46}{space 3}0.000{col 54}{space 4} .0331678{col 67}{space 3} .0595529
{txt}{space 6}gender {c |}{col 14}{res}{space 2} -.012471{col 26}{space 2} .0198935{col 37}{space 1}   -0.63{col 46}{space 3}0.531{col 54}{space 4}-.0514615{col 67}{space 3} .0265195
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0095739{col 26}{space 2} .0212679{col 37}{space 1}   -0.45{col 46}{space 3}0.653{col 54}{space 4}-.0512582{col 67}{space 3} .0321104
{txt}{space 4}religion {c |}{col 14}{res}{space 2}-.0423295{col 26}{space 2}  .025963{col 37}{space 1}   -1.63{col 46}{space 3}0.103{col 54}{space 4}-.0932159{col 67}{space 3}  .008557
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0476776{col 26}{space 2} .0087143{col 37}{space 1}    5.47{col 46}{space 3}0.000{col 54}{space 4} .0305978{col 67}{space 3} .0647574
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.0704707{col 26}{space 2}  .028104{col 37}{space 1}   -2.51{col 46}{space 3}0.012{col 54}{space 4}-.1255535{col 67}{space 3}-.0153879
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-1.207934{col 26}{space 2} .0874474{col 37}{space 1}  -13.81{col 46}{space 3}0.000{col 54}{space 4}-1.379328{col 67}{space 3} -1.03654
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-.2143281{col 26}{space 2} .0868036{col 37}{space 1}   -2.47{col 46}{space 3}0.014{col 54}{space 4}  -.38446{col 67}{space 3}-.0441961
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} .8996585{col 26}{space 2}   .08708{col 37}{space 1}   10.33{col 46}{space 3}0.000{col 54}{space 4} .7289849{col 67}{space 3} 1.070332
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 2.044708{col 26}{space 2} .0883389{col 37}{space 1}   23.15{col 46}{space 3}0.000{col 54}{space 4} 1.871567{col 67}{space 3} 2.217849
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1376951{col 26}{space 2} .0358625{col 54}{space 4} .0826464{col 67}{space 3} .2294102
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 964.88{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit family_edu class income education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,241
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         7
{col 63}{txt}avg{col 67}={res}{col 69}     301.2
{col 63}{txt}max{col 67}={res}{col 69}     1,062

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}   131.90
{txt}Log likelihood = {res}-13710.422{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  family_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0176174{col 26}{space 2} .0073298{col 37}{space 1}    2.40{col 46}{space 3}0.016{col 54}{space 4} .0032513{col 67}{space 3} .0319834
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0012506{col 26}{space 2} .0104401{col 37}{space 1}   -0.12{col 46}{space 3}0.905{col 54}{space 4}-.0217128{col 67}{space 3} .0192117
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0556649{col 26}{space 2} .0091279{col 37}{space 1}    6.10{col 46}{space 3}0.000{col 54}{space 4} .0377745{col 67}{space 3} .0735552
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0439385{col 26}{space 2} .0071406{col 37}{space 1}    6.15{col 46}{space 3}0.000{col 54}{space 4} .0299431{col 67}{space 3} .0579338
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.0183628{col 26}{space 2} .0219563{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0613963{col 67}{space 3} .0246707
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0207117{col 26}{space 2} .0227572{col 37}{space 1}   -0.91{col 46}{space 3}0.363{col 54}{space 4} -.065315{col 67}{space 3} .0238916
{txt}{space 4}religion {c |}{col 14}{res}{space 2}-.0359702{col 26}{space 2} .0274016{col 37}{space 1}   -1.31{col 46}{space 3}0.189{col 54}{space 4}-.0896764{col 67}{space 3} .0177359
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0523752{col 26}{space 2} .0092529{col 37}{space 1}    5.66{col 46}{space 3}0.000{col 54}{space 4} .0342399{col 67}{space 3} .0705105
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.0656771{col 26}{space 2} .0296097{col 37}{space 1}   -2.22{col 46}{space 3}0.027{col 54}{space 4}-.1237111{col 67}{space 3} -.007643
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-1.209655{col 26}{space 2} .0913381{col 37}{space 1}  -13.24{col 46}{space 3}0.000{col 54}{space 4}-1.388674{col 67}{space 3}-1.030635
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-.2094635{col 26}{space 2} .0906623{col 37}{space 1}   -2.31{col 46}{space 3}0.021{col 54}{space 4}-.3871583{col 67}{space 3}-.0317687
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} .9238769{col 26}{space 2} .0909711{col 37}{space 1}   10.16{col 46}{space 3}0.000{col 54}{space 4} .7455768{col 67}{space 3} 1.102177
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 2.078549{col 26}{space 2} .0923757{col 37}{space 1}   22.50{col 46}{space 3}0.000{col 54}{space 4} 1.897496{col 67}{space 3} 2.259603
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1391732{col 26}{space 2} .0365536{col 54}{space 4} .0831743{col 67}{space 3} .2328745
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 885.29{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit family_edu class income employer high_skilled education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,241
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         7
{col 63}{txt}avg{col 67}={res}{col 69}     301.2
{col 63}{txt}max{col 67}={res}{col 69}     1,062

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   133.51
{txt}Log likelihood = {res}-13709.615{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  family_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0171867{col 26}{space 2}  .007385{col 37}{space 1}    2.33{col 46}{space 3}0.020{col 54}{space 4} .0027122{col 67}{space 3} .0316611
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0031144{col 26}{space 2} .0107346{col 37}{space 1}   -0.29{col 46}{space 3}0.772{col 54}{space 4}-.0241538{col 67}{space 3}  .017925
{txt}{space 4}employer {c |}{col 14}{res}{space 2}-.0403028{col 26}{space 2} .0431929{col 37}{space 1}   -0.93{col 46}{space 3}0.351{col 54}{space 4}-.1249593{col 67}{space 3} .0443536
{txt}high_skilled {c |}{col 14}{res}{space 2} .0227027{col 26}{space 2} .0252877{col 37}{space 1}    0.90{col 46}{space 3}0.369{col 54}{space 4}-.0268604{col 67}{space 3} .0722657
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0529143{col 26}{space 2} .0096613{col 37}{space 1}    5.48{col 46}{space 3}0.000{col 54}{space 4} .0339785{col 67}{space 3} .0718502
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0437815{col 26}{space 2} .0071856{col 37}{space 1}    6.09{col 46}{space 3}0.000{col 54}{space 4} .0296979{col 67}{space 3}  .057865
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.0209963{col 26}{space 2} .0220587{col 37}{space 1}   -0.95{col 46}{space 3}0.341{col 54}{space 4}-.0642306{col 67}{space 3} .0222379
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0204422{col 26}{space 2} .0227661{col 37}{space 1}   -0.90{col 46}{space 3}0.369{col 54}{space 4}-.0650629{col 67}{space 3} .0241786
{txt}{space 4}religion {c |}{col 14}{res}{space 2}-.0356672{col 26}{space 2}  .027403{col 37}{space 1}   -1.30{col 46}{space 3}0.193{col 54}{space 4}-.0893761{col 67}{space 3} .0180418
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0517089{col 26}{space 2} .0092679{col 37}{space 1}    5.58{col 46}{space 3}0.000{col 54}{space 4} .0335441{col 67}{space 3} .0698737
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.0670549{col 26}{space 2} .0296798{col 37}{space 1}   -2.26{col 46}{space 3}0.024{col 54}{space 4}-.1252262{col 67}{space 3}-.0088837
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-1.223121{col 26}{space 2} .0927099{col 37}{space 1}  -13.19{col 46}{space 3}0.000{col 54}{space 4}-1.404829{col 67}{space 3}-1.041412
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-.2225923{col 26}{space 2} .0920144{col 37}{space 1}   -2.42{col 46}{space 3}0.016{col 54}{space 4}-.4029373{col 67}{space 3}-.0422474
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} .9108169{col 26}{space 2} .0923127{col 37}{space 1}    9.87{col 46}{space 3}0.000{col 54}{space 4} .7298873{col 67}{space 3} 1.091746
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 2.065374{col 26}{space 2} .0936992{col 37}{space 1}   22.04{col 46}{space 3}0.000{col 54}{space 4} 1.881728{col 67}{space 3} 2.249021
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1395636{col 26}{space 2} .0366562{col 54}{space 4} .0834076{col 67}{space 3} .2335281
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 884.48{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. estimates store edu_2

. 
. 
. 
. coefplot (edu_1) (edu_2) , eform drop(_cons) omitted xline(1, lcolor(black) lwidth(thin)) lpattern(dash) graphregion(fcolor(white)) mlabel format(%9.2f) mlabposition(8) mlabsize(tiny) level(95) keep (class) 
{res}
{com}. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Han\Desktop\document\불평등 연구\주제14_내로남불_교육\social_sciences\data\analysis_1.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}10 Nov 2024, 13:17:47
{txt}{.-}
{smcl}
{txt}{sf}{ul off}